Many video content consumers seek to interact with the video content. For example, users pause, rewind, fast forward and otherwise control their viewing experience. These are well-known concepts, however other types of interaction are likely wanted by many users.
One desired type of interaction is to be able to use a personalized video service or the like to explore what is embedded in the video content. By way of example, a user may wish to find out the identity of an actor in a certain scene, and/or (even if the actor's identity is known), find out something more about that actor, e.g., biographical information. At present, to find out more about the cast of a television show or movie, a user can go to the internet, which includes at least one website that has global information on a per-show basis. There, the user can look up the show on such a site, and look through a gallery of images until find the actor of interest is found.
Rather than manually going to the internet, a service that provided more automated user interaction scenarios (such as to pause a show and request automatic identification of an actor appearing at that time) would need to depend on face recognition. However, face recognition is one of the most challenging tasks for machine learning, because factors such as luminance condition, pose position and facial expression significantly impact the final precision and recall result. Further, face recognition is complex because people age and otherwise change over time, e.g., go from bearded to clean-shaven, sometimes wear a hat, and so forth. Heretofore there has been no known way to provide support for such an automated service.